Power Load Forecast Based on Fuzzy BP Neural Networks with Dynamical Estimation of Weights

Ge, Quanbo; Jiang, Haoyu; He, Meiguang; Zhu, Yanzi; Zhang, Jianmin

doi:10.1007/s40815-019-00796-7

Cited by 14 publications

(7 citation statements)

References 41 publications

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“…At present, the common activation functions in deep learning models mainly include Tanh, ReLu, and Sigmoid functions. e Tanh function expression is shown in formula (11) [13], and the derivative function expression is shown in formula (12) [14]. ere is advantage of improving model training efficiency, but it is prone to zigzag phenomenon.…”

Section: Bridge Structural Damage Locationmentioning

confidence: 99%

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Research on Bridge Structural Damage Identification

Sun

Sheng

et al. 2022

Scientific Programming

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The traditional identification methods have limited ability to identify damage location of bridge structures. Therefore, a bridge structural damage location identification method based on deep learning is proposed. In addition, the sigmoid function is the activation function, and the cross entropy is the cost function. Meanwhile, take the Gaussian noise as the addition method and take the softmax as the classifier. So the constructed SDAE deep learning model can realize damage location identification of the simply supported the continuous beam bridges. Compared with the traditional identification methods of bridge structures, namely BP network and SVM, the proposed method shows higher identification accuracy and antinoise performance. Here, the average identification accuracy of the method for continuous beam bridge is 99.8%. As can be seen that the proposed method is more suitable for practical bridge structure damage location identification.

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Section: Bridge Structural Damage Locationmentioning

confidence: 99%

“…e ReLu function expression is shown in formula (13) [16], and the derivative function expression is shown in formula (14) [17]. Here, the problem of gradient disappearance can be effectively solved.…”

Section: Bridge Structural Damage Locationmentioning

confidence: 99%

Research on Bridge Structural Damage Identification

Sun

Sheng

et al. 2022

Scientific Programming

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“…Some typical models sought in previous studies include the ARIMA model, VAR model and VECM model. Over the past decade, computational power has becoming much more affordable, and the interest among researchers in building machine learning models aiming at offering good forecasts in economics and finance has been well documented (Ge, Jiang, He, Zhu, & Zhang, 2020;Yang & Wang, 2019), including, of course, forecasts of commodity prices for the agricultural market (Abreham, 2019;Ali, Deo, Downs, & Maraseni, 2018;Antwi, Gyamfi, Kyei, Gill, & Adam, 2022;Ayankoya, Calitz, & Greyling, 2016;Degife & Sinamo, 2019;Deina et al, 2021;Dias & Rocha, 2019;Fang, Guan, Wu, & Heravi, 2020;Filippi et al, 2019;G omez, Salvador, Sanz, & Casanova, 2021;Handoyo & Chen, 2020;Harris, 2017;Huy, Thac, Thu, Nhat, & Ngoc, 2019;Jiang, He, & Zeng, 2019;Khamis & Abdullah, 2014;Kohzadi, Boyd, Kermanshahi, & Kaastra, 1996;Kouadio et al, 2018;Li, Chen, Li, Wang, & Xu, 2020, Li, Li, Liu, Zhu, & Wei, 2020Lopes, 2018;Mayabi, 2019;de Melo, J unior, & Milioni, 2004;Melo, Milioni, & Nascimento J unior, 2007;Moreno et al, 2018;Naveena et al, 2017;Rasheed, Younis, Ahmad, Qadir, & Kashif, 2021;dos Reis Filho, Correa, Freire, & Rezende, 2020;Ribeiro & Oliveira, 2011;Ribeiro, Ribeiro, Reynoso-Meza, & dos Santos Coelho, 2019;Ribeiro & dos Santos Coelho, 2020;RL & Mishra, 2021;…”

Section: Introductionmentioning

confidence: 99%

Yellow corn wholesale price forecasts via the neural network

Zhang

2023

ECON

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PurposeForecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.Design/methodology/approachThe authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.FindingsThe final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.Originality/valueThrough the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

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“…In the early stage of fault recognition, the algorithms commonly used for building classification models include neural networks (NNs) [16], support vector machines (SVMs) [17], and their derivative methods. However, these "shallow structure" requires users to input the featureextracted data to effectively distinguish different fault types.…”

Section: Introductionmentioning

confidence: 99%

Novel Rotating Machinery Structural Faults Signal Adaptive Multiband Filtering and Automatic Diagnosis

Xiao

Liao

Wang

et al. 2021

Mathematical Problems in Engineering

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To realize an automatic diagnosis of rotating machinery structure faults, this paper presents a novel fault diagnosis model based on adaptive multiband filter and stacked autoencoders (SAEs). First, to solve the problem where the actual rotating frequency and its harmonics cannot be accurately extracted in engineering applications, an improved adaptive multiband filtering method is designed. This method takes the theoretical rotating frequency as the search center, extracts the maximum within the positive and negative deviation as the actual rotating frequency, and sets a threshold according to the actual value to realize multiband filtering. This method can effectively remove background noise and accurately extract the actual rotating frequency and its harmonics. Second, an unsupervised SAE multiclassification model is established to realize an automatic diagnosis of fault types. This model can automatically extract the in-depth features of the filtered signal and improve the fault classification accuracy. Third, engineering and comparative experiments were carried out to verify the effectiveness and superiority of this model. Results show that the proposed automatic diagnosis model can extract the characteristic components abundantly and accurately recognize rotating machinery structural faults.

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Power Load Forecast Based on Fuzzy BP Neural Networks with Dynamical Estimation of Weights

Cited by 14 publications

References 41 publications

Research on Bridge Structural Damage Identification

Research on Bridge Structural Damage Identification

Yellow corn wholesale price forecasts via the neural network

Novel Rotating Machinery Structural Faults Signal Adaptive Multiband Filtering and Automatic Diagnosis

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